Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
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Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. A 15 minutes seminar which explains the PPGNs models. The paper: https://arxiv.org/abs/1612.00005
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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
- 1. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune 2017 By Safaa Alnabulsi
- 2. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 2
- 3. Motivation Challenges solved in generating Images: High quality images at higher resolutions (227 × 227): Current image generative models often work well at low resolutions (e.g. 32 × 32), but struggle to generate high-resolution (e.g. 128 × 128 or higher), due to many challenges including difficulty in training and computationally expensive sampling procedures. Author | Safaa Alnabulsi 3
- 4. Motivation Challenges solved in generating Images: High resolutions images Realistic and diverse samples within a class Author | Safaa Alnabulsi 4
- 5. Motivation Challenges solved in generating Images: High resolutions images Realistic and diverse samples within a class Does it for all 1000 ImageNet categories Author | Safaa Alnabulsi 5
- 6. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 6
- 7. What is GAN? GAN consists of two models: - Generative model (G): generates new data instances / models the distribution of individual classes. p(x|c) - Discriminative model (D): evaluates them for authenticity/ learns the boundary between classes Author | Safaa Alnabulsi 7
- 8. How does GAN work? GANs are formulated as a game between two networks and it is important (and tricky!) to keep them in balance! Author | Safaa Alnabulsi Cite: Real / Fake 8
- 9. Plug and Play Generative Networks Author | Safaa Alnabulsi 9
- 10. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 10
- 11. Probabilistic interpretation of iterative image generation methods MALA-approx: Metropolis-adjusted Langevin algorithm (MALA )+ Markov chain Monte Carlo (MCMC) which uses the following transition operator: Author | Safaa Alnabulsi 11
- 12. Probabilistic framework for Activation Maximization take a step from the current image xt toward one that looks more like a generic image (an image from any class). take a step from the current image xt toward an image that causes the classifier to output higher confidence in the chosen class. add a small amount of noise to jump around the search space to encourage a diversity of images Author | Safaa Alnabulsi prior conditionjoint model 12
- 13. Probabilistic framework for Activation Maximization Author | Safaa Alnabulsi 13
- 14. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 14
- 15. Methods Author | Safaa Alnabulsi 15
- 16. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 16
- 17. Method | DGN-AM: sampling without a learned prior Deep Generator Network-based Activation Maximization Perform sampling in this lower-dimensional h-space. h in this case represents features extracted from the first fully connected layer (called fc6) of a pre-trained AlexNet on 1000-class ImageNet classification network Author | Safaa Alnabulsi 17
- 18. Method | DGN-AM: sampling without a learned prior Once we trained the network G we find the equation for the MALA algorithm No learned prior No noise Author | Safaa Alnabulsi 18
- 19. Method | DGN-AM: sampling without a learned prior proc: • Sampling in input space h is faster than image space x. • produce realistic images at a high resolution. • It can also produce interesting new types of images that G never saw during training Author | Safaa Alnabulsi cons: • Low mixing speed • Same image after many steps • Lack of diversity 19
- 20. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 20
- 21. Method | PPGN-x: DAE model of p(x) What is DAE (Denoising Autoencoder) ? DAE Noisex + R(x) Author | Safaa Alnabulsi 21
- 22. Method | PPGN-x: DAE model of p(x) Author | Safaa Alnabulsi 22
- 23. Method | PPGN-x: DAE model of p(x) proc: • sampling from the entire model. Author | Safaa Alnabulsi cons: • it models the data distribution poorly. • the chain mixes slowly (sampling in the high-dimensional image space) 23
- 24. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) <-- (This paper‘s model) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 24
- 25. Method | PPGN-h: Generator and DAE model of p(h) Author | Safaa Alnabulsi To address the poor mixing speed of DGN-AM, they incorporate a proper p(h) prior learned via a DAE into the sampling procedure 25
- 26. Method | PPGN-h: Generator and DAE model of p(h) Author | Safaa Alnabulsi The update rule to sample h from this model: 26
- 27. Method | PPGN-h: Generator and DAE model of p(h) proc: • the chain mixes faster than PPGN-x Author | Safaa Alnabulsi cons: • Sample from PPGN-h are qualitatively similar to those from DGN-AM • Samples still lack quality and diversity. ( poor p(h) model learned by the DAE). 27
- 28. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Resuls o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 28
- 29. Generating images with different condition networks | Captioning PPGNs can be flexibly turned into a text-to-image model by combining the prior with an image captioning network, and this process does not even require additional training. Author | Safaa Alnabulsi 29
- 30. Generating images with different condition networks | Multifaceted Feature Visualization Instead of conditioning on a class output neuron, here we condition on a hidden neuron, revealing many facets that a neuron has learned to detect Author | Safaa Alnabulsi 30 Figure 6: Images synthesized to activate a hidden neuron (number 196) previously identified as a “face detector neuron”
- 31. Index Motivation What is GAN? How does GAN work? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 31
- 32. Inpainting Because PPGNs can be interpreted probabilistically, we can also sample from them conditioned on part of an image (in addition to the class condition) to perform inpainting. Author | Safaa Alnabulsi 32
- 33. Index Motivation What is GAN? Probabilistic interpretation of iterative image generation methods Methods and Experiments o PDGN-AM: sampling without a learned prior o PGN-x: DAE model of p(x) o PPGN-h: Generator and DAE model of p(h) Additional Results o Generating images with different condition networks (Captioning , Multifaceted Feature Visualization ) o Inpainting Conclusion Author | Safaa Alnabulsi 33
- 34. Conclusion P&P model generates images in 227x227 which is considered HD in Image generation models. The most useful property of PPGN is the capability of “plug and play”— allowing one to drop in a replaceable condition network and generate images according to a condition specified ( Classs, caption or neuron ) at test time. Usage of PPGNs to synthesize images for videos or create arts with one or even multiple condition networks at the same time. The approach is modality-agnostic and can be applied to many types of data. Author | Safaa Alnabulsi 34
- 35. Author | Safaa Alnabulsi 35
- 36. Author | Safaa Alnabulsi 36 Portrait of Edmond Belamy, 2018, created by GAN (Generative Adversarial Network). Sold for $432,500 on 25 October at Christie’s in New York. Image © Obvious
Editor's Notes
- Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning
- Here are the steps a GAN takes: - The generator takes in random numbers and returns an image. - This generated image is fed into the discriminator alongside a stream of images taken from the actual dataset. - The discriminator takes in both real and fake images and returns probabilities, a number between 0 and 1, with 1 representing a prediction of authenticity and 0 representing fake. So you have a double feedback loop: - The discriminator is in a feedback loop with the ground truth of the images, which we know. - The generator is in a feedback loop with the discriminator. Ref: https://skymind.ai/wiki/generative-adversarial-network-gan
- In Plug and play, to generate images they combine two different types of networks: The Generator can be thought of as generic painter that draws wide variety of images and show them to the conditioner Which looks at the images and tell the generator what to draw next And this process is iterative replaceable conditioner
- for obtaining random samples, they use the Metropolis-adjusted Langevin algorithm (MALA) is a Markov chain Monte Carlo (MCMC) method probability distribution for which direct sampling is difficult. The furture state is based on the current step + gradient + noise
- joint model p(x, y), which can be decomposed into an image model and a clas- sification model:
- In every update they encourage the image to be realistic, class specific and diverse. It mixes fast because we sample in the latent space. I will cover that in the upcoming slides.
- In this paper, they propose a class of models called PPGNs that are composed of 1) a generator network G that is trained to draw a wide range of image types, and 2) a replaceable “condition” network C that tells G what to draw Starting from b
- Instead of sampling in the image space (i.e. in the space of individual pixels) they sample in the abstract, high-level feature space h of a generator G trained to reconstruct images x from compressed features h extracted from a pre-trained encoder E (f). So the input in here is no longer an image but rather a random noise vector .
- Because the generator network was trained to produce realistic images, it serves as a prior on p(x) since it ideally can only generate real images. However, this model has no learned prior on p(h) Explain the update equation: x is a deterministic variable, so we can simplify the model they define a Gaussian p(h) centered at 0 The final h is pushed through G to produce an image sample.
- They train a DAE for images and incorporate it to the sampling procedure as a p(x) prior to avoid fooling examples in here explain what the dae? A trained network which reconstructs an image after adding some random noise to it. We use it to calculate the score function p(x) by subtracting the output from input and divide them by the added noise (squared segma)
- We take here the MALA equation and put the formula from the DAE in first term, and this would represent how we generates images.
- I will be mentioning the mixing speed in the presnetion which means ( "mixing time" of the Markov chain ): The mixing time has a direct impact on sampling quality since, the smaller the mixing time, the faster the convergence of the Markov chain to the stationary distribution, and the smaller the correlation in the samples. In this approach we notice that and says the cons.
- In here they included all three ǫ terms.
- "mixing time" of the Markov chain: The mixing time has a direct impact on sampling quality since, the smaller the mixing time, the faster the convergence of the Markov chain to the stationary distribution, and the smaller the correlation in the samples.
- Inpainting is filling in missing pixels given the observed context regions. The model must understand the entire image to be able to reasonably fill in a large masked out region that is positioned randomly
- Fun Fact!